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公开(公告)号：EP3690477A1
公开(公告)日：2020-08-05
申请号：EP20153076.3
申请日：2020-01-22
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , SHIN, Dongsoo , YEO, Donghun , RYU, Wooju , LEE, Myeong-Chun , LEE, Hyungsoo , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for a V2V communication by using a radar module used for detecting objects nearby is provided. And the method includes steps of: (a) a computing device performing (i) a process of instructing the radar module to transmit 1-st transmitting signals by referring to at least one 1-st schedule and (ii) a process of generating RVA information by using (1-1)-st receiving signals, corresponding to the 1-st transmitting signals; and (b) the computing device performing a process of instructing the radar module to transmit 2-nd transmitting signals by referring to at least one 2-nd schedule.
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公开(公告)号：EP3686811A1
公开(公告)日：2020-07-29
申请号：EP20151832.1
申请日：2020-01-14
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for on-device continual learning of a neural network which analyzes input data is provided to be used for smartphones, drones, vessels, or a military purpose. The method includes steps of: a learning device, (a) sampling new data to have a preset first volume, instructing an original data generator network, which has been learned, to repeat outputting synthetic previous data corresponding to a k-dimension random vector and previous data having been used for learning the original data generator network, such that the synthetic previous data has a second volume, and generating a batch for a current-learning; and (b) instructing the neural network to generate output information corresponding to the batch. The method can be performed by generative adversarial networks (GANs), online learning, and the like. Also, the present disclosure has effects of saving resources such as storage, preventing catastrophic forgetting, and securing privacy.
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公开(公告)号：EP3686809A1
公开(公告)日：2020-07-29
申请号：EP19215143.9
申请日：2019-12-11
申请人： StradVision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： There is provided a method for determining an FL value to be used for optimizing hardware applicable to mobile devices, compact networks, and the like with high precision. The method includes steps of: a computing device (a) applying quantization operations to original values included in an original vector by referring to a BW value and each of FL candidate values, to thereby generate each of quantized vectors, including the quantized values, corresponding to each of the FL candidate values; (b) generating each of weighted quantization loss values, corresponding to each of the FL candidate values, by applying weighted quantization loss operations to information on each of differences between the original values and the quantized values included in each of the quantized vectors; and (c) determining the FL value among the FL candidate values by referring to the weighted quantization loss values and a device using the same.
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公开(公告)号：EP3686792A1
公开(公告)日：2020-07-29
申请号：EP19208315.2
申请日：2019-11-11
申请人： Stradvision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
IPC分类号： G06K9/34
摘要： A method for segmenting an image by using each of a plurality of weighted convolution filters for each of grid cells to be used for converting modes according to classes of areas is provided to satisfy level 4 of an autonomous vehicle. The method includes steps of: a learning device (a) instructing (i) an encoding layer to generate an encoded feature map and (ii) a decoding layer to generate a decoded feature map; (b) if a specific decoded feature map is divided into the grid cells, instructing a weight convolution layer to set weighted convolution filters therein to correspond to the grid cells, and to apply a weight convolution operation to the specific decoded feature map; and (c) backpropagating a loss. The method is applicable to CCTV for surveillance as the neural network may have respective optimum parameters to be applied to respective regions with respective distances.
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55.发明公开
公开(公告)号：EP3637318A1
公开(公告)日：2020-04-15
申请号：EP19184945.4
申请日：2019-07-08
申请人： Stradvision, Inc.
发明人： Kim, Kye-Hyeon , Kim, Yongjoong , Kim, Insu , Kim, Hak-Kyoung , Nam, Woonhyun , Boo, SukHoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A method for learning an object detector based on an R-CNN by using a first to an n-th filter blocks respectively generating a first to an n-th feature maps through convolution operations in sequence, and a k-th to a first upsampling blocks respectively coupled with the first to the n-th filter blocks is provided. The method includes steps of: a learning device instructing the k-th upsampling block to the first upsampling block to generate a (k-1)-st pyramidic feature map to the first pyramidic feature map respectively; instructing an RPN to generate each ROI corresponding to each candidate region, and instructing a pooling layer to generate a feature vector; and learning parameters of the FC layer, the k-th to the first upsampling blocks, and the first to the n-th filter blocks by backpropagating a first loss generated by referring to object class information, object regression information, and their corresponding GTs.
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56.发明公开
公开(公告)号：EP3620987A1
公开(公告)日：2020-03-11
申请号：EP19184054.5
申请日：2019-07-03
申请人： Stradvision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A method for providing an integrated feature map by using an ensemble of a plurality of outputs from a convolutional neural network (CNN) is provided. The method includes steps of: a CNN device (a) receiving an input image and applying a plurality of modification functions to the input image to thereby generate a plurality of modified input images; (b) applying convolution operations to each of the modified input images to thereby obtain each of modified feature maps corresponding to each of the modified input images; (c) applying each of reverse transform functions, corresponding to each of the modification functions, to each of the corresponding modified feature maps, to thereby generate each of reverse transform feature maps corresponding to each of the modified feature maps; and (d) integrating at least part of the reverse transform feature maps to thereby obtain an integrated feature map.
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公开(公告)号：EP3620985A1
公开(公告)日：2020-03-11
申请号：EP19171167.0
申请日：2019-04-25
申请人： Stradvision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A learning method for learning parameters of convolutional neural network (CNN) by using multiple video frames is provided. The learning method includes steps of: (a) a learning device applying at least one convolutional operation to a (t-k)-th input image corresponding to a (t-k) -th frame and applying at least one convolutional operation to a t-th input image corresponding to a t-th frame following the (t-k)-th frame, to thereby obtain a (t-k) -th feature map corresponding to the (t-k) -th frame and a t-th feature map corresponding to the t-th frame; (b) the learning device calculating a first loss by referring to each of at least one distance value between each of pixels in the (t-k) -th feature map and each of pixels in the t-th feature map; and (c) the learning device backpropagating the first loss to thereby optimize at least one parameter of the CNN.
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58.发明公开
公开(公告)号：EP3620979A1
公开(公告)日：2020-03-11
申请号：EP19172861.7
申请日：2019-05-06
申请人： Stradvision, Inc.
发明人： KIM, Kye-Hyeon , KIM, Yongjoong , KIM, Insu , KIM, Hak-Kyoung , NAM, Woonhyun , BOO, SukHoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A learning method for detecting a specific object based on convolutional neural network (CNN) is provided. The learning method includes steps of: (a) a learning device, if an input image is obtained, performing (i) a process of applying one or more convolution operations to the input image to thereby obtain at least one specific feature map and (ii) a process of obtaining an edge image by extracting at least one edge part from the input image, and obtaining at least one guide map including information on at least one specific edge part having a specific shape similar to that of the specific object from the obtained edge image; and (b) the learning device reflecting the guide map on the specific feature map to thereby obtain a segmentation result for detecting the specific object in the input image.
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公开(公告)号：EP3477554A2
公开(公告)日：2019-05-01
申请号：EP18192822.7
申请日：2018-09-05
申请人： StradVision, Inc.
发明人： Kim, Yongjoong , Nam, Woonhyun , Boo, Sukhoon , Sung, Myungchul , Yeo, Donghun , Ryu, Wooju , Jang, Taewoong , Jeong, Kyungjoong , Je, Hongmo , Cho, Hojin
摘要： A learning method for adjusting parameters of a CNN using loss augmentation is provided. The method includes steps of: a learning device acquiring (a) a feature map from a training image; (b) (i) proposal ROIs corresponding to an object using an RPN, and a first pooled feature map by pooling areas, on the feature map, corresponding to the proposal ROIs, and (ii) a GT ROI, on the training image, corresponding to the object, and a second pooled feature map by pooling an area, on the feature map, corresponding to the GT ROI; and (c) (i) information on pixel data of a first bounding box when the first and second pooled feature maps are inputted into an FC layer, (ii) comparative data between the information on the pixel data of the first bounding box and a GT bounding box, and backpropagating information on the comparative data to adjust the parameters.
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公开(公告)号：EP3467720A1
公开(公告)日：2019-04-10
申请号：EP18192817.7
申请日：2018-09-05
申请人： StradVision, Inc.
发明人： KIM, Yongjoong , NAM, Woonhyun , BOO, Sukhoon , SUNG, Myungchul , YEO, Donghun , RYU, Wooju , JANG, Taewoong , JEONG, Kyungjoong , JE, Hongmo , CHO, Hojin
摘要： A learning method for improving performance of a CNN by using Feature Up-sampling Networks is disclosed. The learning method includes steps of: (a) allowing the down-sampling block to acquire a down-sampling image; (b) allowing each of a (1-1)-th to a (1-k)-th filter blocks to respectively acquire each of a (1-1)-th to a (1-k)-th feature maps; (c) allowing a specific up-sampling block to (i) receive a particular feature map from its corresponding filter block, and (ii) receive another specific feature map from its previous up-sampling block, and then rescale a size of the specific feature map to be identical to that of the particular feature map and (iii) apply certain operations to the particular feature map and the rescaled specific feature map to generate a feature map of the specific up-sampling block; and (d)(i) allowing an application block to acquire an application-specific output and (ii) performing a first backpropagation process.
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